Neither the goals nor the procedures of 3D mapping are clearly defined yet
(a) Reference data for mapping
As reference data we used a digital version
of the national topographic map 1:25,000
(Pixelmap) and the vectorized map (VECTOR 25) of this area, both products
of the Swiss Federal Office of Topography.
The planimetric accuracy is given as 3-8 m
(corresponding to the map accuracy), which
is by far worse than the geo-referencing
and object measurement accuracy in
IKONOS imagery. For quality control
of manually measured contour lines we
digitized the 10 m contour lines of the map.
Figure 1. Comparison of the 10 m reference contour lines (black lines) and the manually drawn contour lines
(dashed lines).Left: open area; middle: steep forest area with two small creeks; right: mixed area
(b) Manual drawing of contour lines
10 m contour lines were measured in an area of 4 x 5.5 sqkm by an experienced
stereo operator of our group using Stereo
Analyst, a 3D feature extraction tool
of the ERDAS IMAGING system.
For the visual quality evaluation we
defined three sub-areas with special
land use patterns (Figure 1: open area
(left), forest (middle) and mixed flat
area (right)). As expected, we got the
best results for an open area with only
a few houses and less good results in
the steeper woodland. The IKONOSderived
contours resulted in a distinctive
smoothing effect of the smaller geomorphological
features. Therefore the
problem is not so much the limited
metric accuracy of the IKONOS-derived
contours (the reference contours also
had only an accuracy of 3-4 m), but
the loss of geo-morphological detail.
This of course is also caused by the fact
that trees and bushes are restricting an
accurate interpretation of the scene.
(c) Object extraction
We used the Stereo Analyst also for
the extraction of all map features. The
identification and mapping of buildings
and roads/streets was done by our own
stereo operator. But for the classification
of roads/streets and for the identification
of several individual features special
knowledge of an experienced topographer
is required. Here we received the
support from our Federal Office of
Topography (swisstopo) in form of one
professional operator, familiar with the
map legend of the Swiss topo-maps.
Both operators did not have any preinformation
about the test area and
did not use the reference data as preinformation
for the measurements.
Figure 2 gives an overview of the
measured buildings, roads/streets,
railway, small airport, forests and
single trees of the test area.
Figure 2: Overview of the measured buildings, roads/streets, railway,
small airport, forests and single trees of the test area Thun.
Figure 3: Overview of a sub-area of the urban test area. Filled colour:
VECTO R 25, contours: IKONOS - extracted houses.
(c1) Identification and
mapping of buildings
In the map buildings are presented by
their footprints. Our operator measured the buildings in 3D, but for comparison
only the 2D footprint of the roofs could
be used. For a more detailed analysis we
sub-divided the whole area in two kinds
of sub-areas: industrial (160 buildings)
and residential areas (165 buildings). For
quality control we compared the extracted
shapes of the houses with the VECTOR
25 data set visually. The main focus was
on the identification of houses and their
shape and less on the metric analysis of
their correct position. The results are
classified into the following 6 categories: Equal: shape and position of the
houses considered as equal Partial loss: parts of the houses
are missing, same position Total loss: the house could
not be extracted Forest: the house is covered by trees Different position: same
shape, different position Improvement: the extracted
shape or position is better.
In case of differences in position of an
individual building it can be assumed
that the IKONOS mapping gives better
planimetric accuracy than the given,
cartographically modified (generalized
and shifted) building data (see Figure 3).
Table 1 gives an overview of the results
of the visual quality control of the
extracted buildings. For a fully detailed
analysis of the
differences,
an inspection
trip into the
field would
be necessary.
The results
clearly show
that the object
identification
from IKONOS
imagery is not
reliable enough.
The approximate mapping of the centerline
of the roads was done by our operator
without cartographic experience. In urban
areas it was more difficult to measure the
centerline than outside the town. Without an
in-depth background in national mapping
it was not possible for her to classify the
streets in relation to the official classes of
the national map. Some parts of smaller
streets were covered by high buildings
and trees. In such areas the interpreter
tried to guess the run of the streets by
analyzing the surrounding structure of the
houses and trees. Altogether 163 street
segments were analyzed. 11 (7%) of them
could not be detected by our operator.
The classification of the streets and roads
was investigated by two cartographic
specialists. The main criterion for the
classification was the width of the streets.
We have 6 classes of roads: R1: >6m, R2:
4.2 – 6m, R3: 3 – 4.2m, R4: <4m, R5:
dirt roads and R6: trails. Because of the
limiting 1m resolution of the used images,
it is very often not possible to distinguish
between two neighboring classes. The
images were taken in December, a period
when dirty roads and trails are not used
regularly and therefore it was difficult
to identify them in several cases. The
highway, railway rails and traffic circles
could be easily identified and measured.
In future, additional features like
pavements will be taken into
account by swisstopo for a detailed
classification. Such small details
cannot be extracted from 1m resolution
satellite images any more.
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